Markov Chain Monte Carlo

Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers ICLR 2024 Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo COLT 2024 Fast parallel sampling under isoperimetry COLT 2024 Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm COLT 2024 Gaussian Cooling and Dikin Walks: The Interior-Point Method for Logconcave Sampling COLT 2024 Geometric No-U-Turn Samplers: Concepts and Evaluation PGM 2024 Gradient-based Discrete Sampling with Automatic Cyclical Scheduling NeurIPS 2024 Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models NeurIPS 2024 Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting ICLR 2024 Langevin Monte Carlo for strongly log-concave distributions: Randomized midpoint revisited ICLR 2024 Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk NeurIPS 2024 Neural Markov Random Field for Stereo Matching CVPR 2024 Neuro-Symbolic Data Generation for Math Reasoning NeurIPS 2024 On Convergence of Federated Averaging Langevin Dynamics UAI 2024 Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games COLT 2024 Piecewise deterministic generative models NeurIPS 2024 Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo ICLR 2024 QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation NeurIPS 2024 Robust exploration with adversary via Langevin Monte Carlo L4DC 2024 Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier COLT 2024 The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models NeurIPS 2024 The power of an adversary in Glauber dynamics COLT 2024 Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy ICLR 2024 Value-Based Abstraction Functions for Abstraction Sampling UAI 2024 Walking the Values in Bayesian Inverse Reinforcement Learning UAI 2024